Trends in electronic health record metadata use for management purposes

Abstract

Objective

This study aims to analyze hospitals' adoption and integration of electronic health record (EHR) metadata into their management processes.

Design

The study compares the rates of EHR metadata utilization across various hospitals over time. Hospitals' self‐reported use of EHR metadata is drawn from the AHA‐IT Supplements from 2018 to 2020. An analysis of metadata utilization by EHR vendors is also provided.

Method

The study uses Bass diffusion modeling to estimate EHR adoption parameters by fitting adoption rate data from 2018 to 2020, using Excel Solver to minimize prediction errors. The estimated internal and external influence coefficients reveal which factor primarily drives adoption, while the diffusion model enables future projection of tipping point and adoption level.

Results

Analysis of EHR metadata utilization rates from 2018 to 2020 find a significant trend towards the integration of this data into hospital management practices. Among health systems responding to the items of interest, 69% of them are already using EHR metadata, and it is projected that nearly all will do so by 2035. Further, metadata use varied significantly depending on the vendor.

Discussion

The study underscores that hospital managers' intrinsic motivations, rather than external demands, are driving EHR metadata. As innovations with greater intrinsic appeal spread more rapidly and have greater staying power, EHR metadata use will continue to grow. These trends are indicative of the growing importance of EHR metadata in management decision‐making, clinical quality improvement, and optimizing workforce efficiency.

Conclusions

EHR metadata holds great promise as a managerial and health service research source. The tools' utilities would be enhanced if EHR vendors created uniform metrics.

1. INTRODUCTION

In the evolving landscape of healthcare management, the utilization of hospital electronic health record (EHR) systems' metadata has emerged as a potential tool in enhancing operational efficiency and patient care. ¹ , ² EHR metadata refers to metrics collected about end‐users actions performed in the system in an unobtrusive and granular (i.e., frequent) fashions. Metadata derived metrics are commonly designed to assess performance and represent an innovation in health system management and research capabilities. ³ Administrators are now able to track clinical staff members' activities in very granular and comprehensive fashions with relatively little effort compared to classic time‐motion studies. ⁴ Moreover, the emergence of sophisticated analytic algorithms such as artificial intelligence allows managers to make powerful inferences about what activities measured using such metadata impact effectiveness and efficiency. ⁵

While the promise of EHR metadata for improving both organizational and clinical outcomes is significant, the current use levels have not been widely promulgated and the likely uptake trends for the technology are unclear. ⁶ EHR vendors offer a variety interfaces for accessing metadata and some have already created built‐in metrics and dashboards for managers to use. ⁷ However, the development of metadata dashboards and has not been uniform and there is not any current regulation standardizing variable/metric development. ⁷ Lacking standardized variables makes studying providers' work behaviors difficult if they are in organizations with disparate EHR systems problematic. Moreover, access to metadata varies across vendors with some having well‐developed interfaces and calculated fields while others do not even make the raw data available; thus creating even more differences in the application of the information. ⁸ Therefore, it would be beneficial to know how many hospitals are currently using metadata for management purposes and what future uptake is likely to look like.

The study at‐hand has three aims. First, we quantify and graphically depict the historic trend of EHR metadata use for management purposes among U.S. hospitals. Based on that information, we extrapolate future utilization trends and discuss the two factors that drive the diffusion process—intrinsic and external social influences. ⁹ Second, based on the derived models, the most probable time horizon for achieving ubiquitous EHR metadata utilization is forecast. Lastly, the variation of EHR metadata availability by vendors is described and discussed.

The study's contributions inform hospital administrators, health policy makers, and EHR system developers by providing insights into EHR metadata's evolving role in hospital management. For administrators, the findings suggest the larger vendors have more fully developed metadata tools. Policy makers involved in healthcare regulation can use these insights to guide EHR meaningful use and certification standards to increase metadata's availability and utility. EHR developers can gain a clearer understanding of the market's demand for metadata tools and tailor their products to better meet the requirements of healthcare providers. Together, these contributions aim to optimize healthcare delivery and management through informed use of EHR metadata.

2. BACKGROUND

2.1. The use of EHR data as a health services research and management

EHR metadata has emerged as an important area of health services research and management, providing insights into the operational dynamics of providers in healthcare facilities. ¹⁰ The metadata fields, derived from EHR systems, offer a granular view of clinical workflows and have the potential to reduce end‐users' documentation burdens and identify differing workflows' impacts. ⁸ EHR metadata are utilized to measure various aspects of healthcare delivery, including physician workload, patient care quality, and operational efficiency across various settings. ¹¹ By leveraging standardized metrics, learning health systems can achieve more accurate benchmarking and cross‐institutional comparisons, facilitating improvements in both clinical and administrative outcomes. However, a 2023 literature review ¹² of 102 articles found there is not yet a set of standardized metadata variables available across EHR vendors. Moreover, many of the research articles in the review relied upon investigator developed variables creating potential issues in replicating the results. To address these issues, a working group has issued guidance for generating, recording, and reporting EHR metadata. ¹³ Collectively, the variance in how EHR metadata is used and its availability make tracking the technology's diffusion an important task.

2.2. The technology diffusion model

Rogers ⁹ created the technology diffusion theory that describes how innovators (i.e., first adopters), early adopters, early majority, late majority, and laggards' take‐up new products. Further research by Bass ¹⁴ statistically modeled the factors that forecast new technologies' diffusion rates, maximum market share, and tipping point as a function of External and Internal Influences. External influences, frequently referred to as innovation factors, are driven by information source(s) from outside the potential adopter's social system. Internal influences from an end‐user's own assessments or interpersonal networks are often referred to as social contagions in the diffusion literature. ¹⁵

The social components of diffusion rates, rather than economic or external factors, have a larger impact on an end‐user's decisions to adopt a technology—ceterus paribus. ¹⁶ It is generally accepted that more rapid products diffusion rates are driven by social contagion. In other words, decision‐makers' internal adoption motivations are a function of their exposure to other trusted user's knowledge, attitudes, or behaviors concerning the new product. Researchers have offered a variety of theoretical views of social contagion, including social learning under uncertainty, social‐normative pressures, competitive concerns, and performance network effects. ¹⁷ In the common parlance of the internet, the most rapid adoption rate is ‘going viral’. ¹⁸

Bass ¹⁴ was the first to develop marketing applications for diffusion models of consumer goods such as washers and dryers. The models were developed to predict the uptake of products based on the influence of various types of advertising campaigns. One key feature of Bass modeling is it predicts how many customers will eventually adopt a new product or what share of a market for existing products any new item will achieve.

3. METHODS

Data for the current analyses were drawn from the American Hospital Association's (AHA's) annual survey and the Information Technology Supplement for 2018, 2019, and 2020. The study has two analyses. The first utilizes Bass modeling, a product diffusion model, to compare rates of EHR metadata utilization for any management purpose across the 3‐year period. Next, a sub‐analysis of which EHR vendors were most commonly making EHR metadata available for use management purposes was conducted. Each method is discussed in turn.

3.1. Diffusion estimation technique

We applied Bass diffusion modeling to estimate key EHR adoption parameters, specifically internal and external influence coefficients and forecasted uptake rates. The formula for calculating the percentage of adopters for an new product or innovation at any point, using discrete time notation, can be written as, ¹⁹ where:

$$F\left(t\right)=\left(1-{\mathrm{e}}^{-\left(p+q\right)t}\right)/\left(1+\left(\frac{q}{p}\right){\mathrm{e}}^{-\left(p+q\right)t}\right)$$ (1)

F(t) is the number of adoptions occurring in period t, p is the coefficient of innovation, capturing the external factors influencing adoption decisions, and the effect of time invariant external influences, q is the coefficient of imitation or social contagion, capturing the rate of adoption as it gradually rises until it hits a maximum at N/2, and then it declines (as non‐users get increasingly hard to find and, therefore, to induce to adopt the technology), and t is period of measurement.

The model has several useful features for making adoption forecasts. For example, given multiple time point measurements, it is possible to solve for p and q using linear optimization. The parameters p and q provide information about the diffusion rate. A high value for p suggests that the diffusion will have a quick start but also tapers off quickly. A high value of q is typical of a product diffusion that starts slow but accelerates later. When q is larger than p, the cumulative number of adopters F(t) + F(t − 1) follows the type of S‐shaped curve commonly observed for highly innovative or expensive products that require extended timeframes before they are widely used. When q is smaller than p, the cumulative number of adopters follows an inverse J‐shaped curve often observed for less risky innovations, such as the adoption of consumer durables with marginal innovations (e.g., flat screen televisions). Once p and q are known, the time (t*) at which the peak adoption rate occurs (i.e., the period when the largest number of individuals adopts) can be calculated as ²⁰ :

$$t^{*}=\ln \left(q/p\right)/\left(p+q\right)$$ (2)

This calculation is commonly referred to as the inflection or ‘tipping point’ ²¹ , ²² when the diffusion paradigm becomes self‐sustaining.

After three or more periods of user innovation uptake measurement, researchers can estimate p and q using the basic Bass model (Equation 1). In the case of EHR metadata use for management purposes, the AHA‐IT survey supplement explicitly asks if the health system is using such information. These studies are described in Section 4. These coefficients are critical for understanding how intrinsic motivations within hospitals and external pressures influence the rate of EHR metadata adoption. The analysis extends to forecasting future adoption rates, pinpointing the sustainability ‘tipping point’, and determining the maximum market penetration based on current trends. To validate our findings and ensure robustness, we employed chi‐square tests for categorical data analysis, examining the relationship between hospital characteristics and their propensity to adopt EHR metadata. This multifaceted approach allows for a comprehensive understanding of the dynamics at play in the adoption of EHR metadata in hospital management processes. Given the point estimates used in our models, it is possible to empirically derive the diffusion curves' historical shape, potential future trends, and the external (p) and internal (q) influence coefficients.

The statistical analysis and forecasting were conducted in Microsoft Excel™ using the linear optimization tool (i.e., Solver). The objective was to have unique estimates for the external (p) and internal (q) influence coefficients that estimated the known adopter percentages of EHR metadata uses as closely as possible for all 3 years. The objective function was the summed differences between predicted and actual EHR metadata utilization levels for all known years, and the target value was zero—or as close to zero as possible. One constraint was applied to the optimization routine. The difference between the actual and estimated percentages of adopters for any observed year should be less than 0.5% in absolute terms for robust forecasting results. ²³

3.2. Analysis of EHR vendors' metadata use rates

For the vendor analysis, crosstabulation was used to examine the relationship between different EHR vendors and the utilization of EHR metadata for management purposes. The survey responses were categorized by vendor and whether metadata was used or not, resulting in counts and percentages for each category. Responses indicating ‘not applicable’ or ‘unsure’ were omitted. Expected counts were calculated to compare with observed counts, providing insight into the distribution of metadata usage across vendors. A Pearson Chi‐Square test was conducted to determine the statistical significance of the association between EHR vendor type and metadata usage.

4. RESULTS

Table 1 describes the structural characteristics of the sample. Table 2 compares hospitals that report using EHR metadata to those that do not. Note these two tables are intended to provide an overview of the characteristics of hospitals in the study and their effect on adoption rate. Therefore, only year 2020 data are shown. For bed size, a strong preference for adoption was observed in larger hospitals, with 86% of large bed size hospitals being adopters compared to only 14% non‐adopters. Medium and small bed size hospitals showed a decreasing trend in adoption rates, indicating a potential resource‐based disparity. The chi‐square test results further substantiate these findings, showing a highly significant difference across bed sizes (χ ² = 108.678, p < 0.001). Similar patterns of significant associations were found when analyzing system membership, for‐profit status, teaching hospital status, and Joint Commission accreditation, each demonstrating varying degrees of adoption preference and statistical significance. These results highlight the influence of organizational size, profit orientation, educational involvement, and accreditation status on the adoption of new technologies within hospital settings.

TABLE 1.

Sample characteristics for health systems included (2020 survey year).

	Count	Percent
Hospital size (beds)
Large (1–99)	570	19.78
Medium (100–299)	933	32.37
Small (300 or more)	1379	47.85
System affiliation
No	806	27.97
Yes	2076	72.03
Profit status
For‐profit	115	3.99
Nonprofit	2767	96.01
Teaching hospital
No	2685	93.16
Yes	197	6.84
Joint commission accreditation
No	874	30.33
Yes	2008	69.67

TABLE 2.

Comparison of meta‐data adopters versus no‐adopter characteristics 2020.

	Adopter		Non‐adopter		Chi‐square	p‐value
	Count	Percent	Count	Percent
Bed size
Large	491	86	79	14	108.679	>0.001
Medium	642	69	291	31
Small	857	62	522	38
System member
No	380	47	426	53	251.173	>0.001
Yes	1610	78	466	22
Profit status
For‐profit	67	58	48	42	6.523	>0.011
Nonprofit	1923	69	844	31
Teaching hospital
No	1812	67	873	33	44.915	>0.001
Yes	178	90	19	10
Accredited by joint comm.
No	493	56	381	44	93.808	>0.001
Yes	1497	75	511	25
Totals	1990	69	892	31

Using Equation (1) and linear optimization, the coefficients of external (p) and internal (q) influences were estimated based on the time periods of 2018–2020. The summed differences for the predicted and actual percentages was very low (calculation = 0.00007) indicating a robust model. Table 3 presents the ‘best’ estimate for the diffusion scenario based on external (p) and internal (q) influence coefficients, the ratio of external to internal influence (p/q), and their tipping points (Equation 2). The projected adoption levels through 2035 are calculated to be 99.35%—near total penetration. The scenario based on the 2018–2020 estimate yield a curve that is indicative that the technology is having a rapid early adoption phase that slows over time—the inverse J‐curve.

TABLE 3.

Metadata utilization coefficient estimates, tipping point, and 2020 adoption rates.

Estimate based on 2018–2020 AHA‐IT	External diffusion coefficient (p)	Internal diffusion coefficient (q)	p/q ratio	Tipping point	2018 adoption percent	2019 adoption percent	2020 adoption percent
All vendor	0.1349	0.1603	0.8415	t = 0.5845	50.71%	60.67%	69.05%

The external diffusion coefficient estimate for EHR metadata use was relatively large (p = 0.1002) compared to other medical equipment technologies'—such as ultrasound imaging (p = 0.000), mammography (p = 0.000), and EHRs themselves (p = 0.0054)—all of which diffused relatively quickly for new products. ²⁴ , ²⁵ , ²⁶ Therefore, the uptake of EHR metadata for managerial and health services research purposes was relatively fast and has moved into the ‘early or late majority’ phase.

Compared to other medical technologies that also diffused rapidly, such as ultrasound imaging (q = 0.510; cf. the current study's result q = 0.1024) and mammography (q = 0.738; cf. the current study's result p = 0.1024), the internal influence coefficients for EHR metadata use is relatively low. In order to accelerate a technology's diffusion beyond its current level it would be necessary to increase the internal or social contagion factors that influence adoption decisions. Given the relatively rapid start, the question becomes, “will new adoptions rates drop to a point where market saturation is not achieved?”. The forecasts do show adoption rates slowing, but it appears that EHR metadata use will become a ubiquitous feature in health system management. The implications of the models' results are discussed next.

To explore vendor‐specific adoption dynamics, we calculated metadata utilization coefficients, tipping points, and annual adoption percentages for each major EHR vendor. These results, summarized in Table 4, underscore significant variation in external and internal diffusion influences. For instance, Epic demonstrated the highest adoption acceleration due to its robust metadata tools, while smaller vendors like Allscripts/Eclypsis exhibited a slower uptake, suggesting resource disparities. These dynamics are further visible when comparing overall market share for metadata use across vendors.

TABLE 4.

Metadata utilization coefficient estimates, tipping point, and 2020 adoption rates.

Estimate based on 2018–2020 AHA‐IT	External diffusion coefficient (p)	Internal diffusion coefficient (q)	p/q ratio	Tipping point	2018 adoption percent	2019 adoption percent	2020 adoption percent
Allscripts/Eclypsis	0.0129	0.4277	0.0302	t = 7.9456	13.64%	17.83%	28.91%
Cerner	0.1631	0.5097	0.3200	t = 1.6938	75.40%	88.67%	91.55%
Epic	0.3613	0.2173	1.6629	t = −0.8790	84.40%	92.13%	94.40%
Meditech	0.0680	0.1820	0.3739	t = 3.9353	34.51%	37.74%	51.15%
Others	0.0083	0.4262	0.0194	t = 9.0730	8.84%	12.25%	19.29%

The crosstabulation of inpatient EHR vendors by metadata use for management reveals significant variations across platforms (see Table 5). Epic is the predominant vendor with 49.0% of the total EHR metadata use, followed by Cerner at 32.1%. Meditech accounts for 11.1% of the metadata use. Two hundred twenty‐two health systems use Epic's metadata for management purposes out of the total 434 instances reported in 2020 across all organizations. Epic's outsized metadata use is consistent with other studies finding that the vendor is used in a disproportionate number of research studies. ²⁷ Conversely, CPSI and Allscripts/Eclypsis have lower metadata usage rates at 2.0% and 1.9%, respectively. Vendors like Athenahealth and MEDHOST show minimal metadata use, each under 1%. Notably, a substantial portion of systems, categorized under “All Other,” which includes several smaller vendors, contributes to 29.9% of the “No” responses regarding metadata use. Based on the differences between ‘observed’ and ‘expected’ counts, larger EHR vendors like Epic and Cerner in metadata utilization for management purposes, with smaller vendors having limited adoption.

TABLE 5.

Inpatient EHR vendors by metadata use for management crosstabulation ^{a , b} .

Vendor	Yes	No	Total
Allscripts/Eclypsis
Count	37	91	128
Expected count	88.4	39.6	128.0
Percent of total EHR MD use	1.9%	10.2%	4.4%
Cerner
Count	639	59	698
Expected count	481.9	216.1	698.0
Percent of total EHR MD use	32.1%	6.6%	24.2%
Epic
Count	977	58	1035
Expected count	714.6	320.4	1035.0
Percent of total EHR MD use	49.0%	6.5%	35.9%
Meditech
Count	222	212	434
Expected count	299.7	134.3	434.0
Percent of total EHR MD use	11.1%	23.7%	15.0%
Self‐developed
Count	6	35	41
Expected count	28.3	12.7	41.0
Percent of total EHR MD use	0.3%	3.9%	1.4%
CPSI
Count	39	126	165
Expected count	113.9	51.1	165.0
Percent of total EHR MD use	2.0%	14.1%	5.7%
MEDHOST
Count	9	34	43
Expected count	29.7	13.3	43.0
Percent of total EHR MD use	0.5%	3.8%	1.5%
Athenahealth
Count	13	11	24
Expected count	16.6	7.4	24.0
Percent of total EHR MD use	0.7%	1.2%	0.8%
All other c
Count	50	267	317
Expected count	218.9	98.1	317.0
Percent of total EHR MD use	2.5%	29.9%	11.0%
Totals
Count	1990	893	2885
Expected count	1990	893	2885

^a Crosstab for those responding definitively ‘Yes’ or ‘No’ to the ‘Any Metadata Use’ item. Count is the observed frequency and expected count is the expected frequency if the null hypothesis that adoption rate is the same across platforms (address comment #5).

^b Pearson Chi‐square = 1324.086, df = 8; p < 0.001.

^c All other EHR vendors with less than five health systems using metadata: eClinical Works, GE, McKesson, MED3000, NextGen, QuadraMed, Sage, Siemens, HMS, Healthland, Vitera, Evident, Allscripts, Prognosis, MedWorx.

5. DISCUSSION

The exploration of EHR metadata adoption in hospital management reveals a trend towards broad integration of these systems into healthcare practices. Based on Bass diffusion modeling, there is a swift uptake of EHR metadata usage among a few vendors, projected to reach near‐universal levels by 2035. The integration of vendor‐specific dynamics, as outlined in Tables 4 and 5, underscores the critical role that EHR vendors play in shaping metadata adoption trajectories. Epic and Cerner's dominance in metadata adoption highlights their advanced toolsets, which facilitate easier integration into hospital workflows. However, the lagging adoption rates among smaller vendors point to systemic inequities that may disproportionately affect less‐resourced hospitals. These findings reinforce the need for standardized metadata tools and equitable access across all vendors to ensure consistent improvements in healthcare delivery and management.

Furthermore, the trajectory analysis demonstrates that intrinsic motivators within hospital systems, as reflected in the internal diffusion coefficients, play a more significant role in accelerating metadata adoption than external pressures. This insight calls for targeted strategies to foster organizational buy‐in and streamline metadata integration processes across varying vendor platforms. Hospitals are increasingly recognizing the value of leveraging big data to drive decisions that enhance operational efficiencies and patient outcomes.

5.1. Key drivers of adoption

Several factors contribute significantly to the rapid diffusion of EHR metadata among hospitals. The cost‐effectiveness of utilizing existing data reduces the financial barriers typically associated with new technology implementations, making EHR metadata an attractive option for many hospitals. The groundwork laid by researchers in developing management‐focused metrics further decreases the need for hospitals to invest heavily in new metric development, allowing for more widespread and economically feasible adoption. Moreover, EHR vendors facilitate this trend by improving the accessibility and usability of metadata through advanced dashboards and decision‐support tools, thus simplifying the integration process for hospital management.

5.2. Challenges and limitations

However, the adoption of EHR metadata is not without challenges. The lack of standardization across different EHR systems poses a significant barrier, complicating the implementation of best practices and benchmarking across healthcare institutions. Furthermore, there is a risk that staff might perceive these systems as intrusive, potentially leading to resistance that could impede the adoption and effectiveness of these technologies. It is crucial for hospitals to address these concerns by fostering a culture of transparency and involving clinical staff in the development and implementation phases.

5.3. Benefits of expanded use

The potential benefits of expanding EHR metadata use within hospital management are substantial. Enhanced operational efficiency can be achieved by utilizing metadata to conduct more detailed analysis of both clinical and administrative operations. Such improvements can lead to optimized resource allocation, minimized waste, and better patient management. In terms of clinical outcomes, EHR metadata can provide insights that help identify effective practices and highlight areas needing improvement, thereby directly improving patient care. Additionally, aligning provider workflows with insights derived from EHR metadata might reduce provider burnout, thus enhancing overall workplace satisfaction and efficiency.

5.4. Vendor effects

The two biggest EHR vendors (Epic and Oracle/Cerner) provide more structured metadata extracts than most other firms. Given these vendors tend to provide services to larger hospital systems and academic medical centers, smaller facilities and their administrators may be systematically disadvantaged in the information management domain. There is an opportunity for the EHR certification and meaningful use programs to address this disparity.

6. CONCLUSIONS

The study's results reveal critical disparities in metadata adoption among EHR vendors, with far‐reaching implications for healthcare management. To bridge these gaps, EHR vendors must prioritize the development of user‐friendly, standardized metadata tools, particularly for smaller health systems. Policymakers, too, have a role to play by revising certification standards to mandate uniformity in metadata accessibility and functionality. While the path to widespread adoption is fraught with challenges such as the need for standardization ²⁸ and potential staff resistance, the overarching benefits provide compelling reasons for continued adoption. To maximize the potential of EHR metadata, it will be essential for vendors, administrators, and policymakers to collaborate closely, addressing these challenges and paving the way for optimized healthcare delivery.

7. FUTURE RESEARCH DIRECTIONS

Further research is essential to expand upon the findings of the current study and address the emerging challenges and opportunities EHR metadata presents. Future studies should focus on developing standardized methodologies for the collection, analysis, and reporting of EHR metadata to enhance comparability and benchmarking across different healthcare systems. This includes investigating the impact of standardized data structures on the adoption and effectiveness of EHR metadata in hospital management. Additionally, there is a need to explore the long‐term effects of EHR metadata integration on clinical outcomes and operational efficiencies, particularly in how it influences patient care protocols and staff workflows.

Another promising area for future research lies in the examination of the socio‐technical barriers to EHR metadata adoption, including organizational culture, staff resistance, and privacy concerns. Qualitative studies that explore the perceptions and attitudes of healthcare staff towards EHR metadata could provide deeper insights into the factors that facilitate or hinder effective implementation. Moreover, studies could evaluate the economic impact of EHR metadata adoption, including cost–benefit analyses and return on investment, to better understand the financial implications for healthcare institutions. By addressing these areas, future research can contribute significantly to the strategic deployment of EHR systems, ultimately leading to enhanced healthcare delivery and patient outcomes.

The study has two major limitations. The first limitation is that the AHA‐IT Supplements has a lower response rate than the standard annual survey. A concern is the respondents may be more technologically inclined than non‐respondents; therefore, the estimates presented are limited to that sample rather than the entire U.S. hospital population. As a result, the estimates may be overly optimistic. Second, the survey only asked hospital administrators if they were using EHR metadata. There are likely to be other EHR metadata users that may not be polled by the AHA. Omitting these users underestimates the use of EHR metadata to a degree.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known conflicts of interest to disclose. This research was conducted independently, and no external influence, financial or otherwise, has impacted the design, execution, or reporting of this study.

Supporting information

Appendix S1: Supplementary information.

Xu N, Badwaik I, Lee G, Ford EW. Trends in electronic health record metadata use for management purposes. Learn Health Sys. 2025;9(3):e70001. doi: 10.1002/lrh2.70001